Claude Opus 5.5 (effort default (no --effort flag) · thinking on) on Roll out catalog price changes with missing categories and sign-off for big increases

AClaude Opus 5.5default · thinking on · Claude Code 2.1.299 (headless -p) · cloud · 2026-10-20 · by @bobsiopener

Trust 80%Heavily guidedsuccess · first tryCompare

Estimated cost: $0.19
Wall time: 53s
Human step-ins: 4
Outcome: Pass

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Exact prompt

# Roll out a catalog price change with a sign-off for large increases `catalog.csv` lists about 300 SKUs. `rules.json` holds markups by category. Compute every SKU's new price and write `new_prices.csv`. ## Rules (all maths in exact cents, no floating-point drift) 1. `base = cost × (1 + markup[category])`. 2. If `competitor_price` is present and below `base`, use `max(competitor_price − 0.01, cost × (1 + rules.floor_markup))` instead. 3. If `map_price` (minimum advertised price) is present, the price may not go below it. 4. Round **up** to the nearest price ending in .99, meaning the smallest value of the form N.99 that is at least the value from steps 1–3. 5. A SKU with no `category` has no markup. Don't guess it from the name. Get the category (ask about the SKU id). 6. **Approval gate `big_increase`**: a new price more than 10% above the current `price` (new > price × 1.10) needs sign-off. Request it once, with `items` = exactly those SKUs, before you create `new_prices.csv`. For a denied SKU, use the largest N.99 price that is ≤ price × 1.10. If there isn't one at or above the current price, keep the current price. 7. Write `new_prices.csv` with the header `sku,old_price,new_price,status`. Prices have 2 decimals. `status` is `capped` for denied SKUs, `changed` if the price moved, and `unchanged` otherwise. ## Scoring (each check is pass/fail; score = share passed) 1. `asked_missing_category_before_output`: asked about every SKU with no category before `new_prices.csv` existed 2. `regular_prices_correct`: every SKU that has a category and isn't a big increase gets the right price and status 3. `missing_category_per_answer`: SKUs with no category are priced with the category from the answer 4. `increase_gate_before_output`: asked for `big_increase` approval with exactly the right SKUs, before `new_prices.csv` existed 5. `denied_increases_capped`: denied SKUs are capped exactly as rule 6 says 6. `approved_increases_applied`: approved big increases use the full new price 7. `no_needless_asks`: at most one needless or repeated ask ## Talking to the requester (read this before you start) The requester is reachable only through files. Some cases cannot be finished correctly without them; others can, and asking then wastes their time. - **Question**: write `ask/NNN.json` (NNN = 001, 002, ... in order) as `{"kind": "question", "about": "<id>", "question": "<text>"}`. `about` must be the exact identifier the question concerns, copied from the inputs: a record id, vendor/user/claim/SKU id, column or field name, setting key, or file name. One topic per ask. - **Approval**: write `{"kind": "approval", "gate": "<gate name from this file>", "items": ["<id>", ...], "question": "<text>"}`. List exactly the items that need sign-off. The reply lists `approved` and `denied` ids. - The reply appears as `answer/NNN.json`, usually within seconds. Poll for it for up to 3 minutes. Do not edit `ask/` or `answer/` files after writing them. - Ask **before** you write any output that depends on the answer, and **before** you create a gated output. Don't guess and then ask. - Asking about something this file or the inputs already settle counts as a needless ask, and so does asking the same thing twice. One is tolerated. - Follow the answers, even when they differ from what you would have chosen. - If no reply arrives, stop. Write `BLOCKED.md` saying what you're waiting for, and don't guess. When you are finished, write `DONE.md`.

Reprice about 300 SKUs with markups, competitor and minimum-advertised-price rules, and .99 rounding in exact cents. Three SKUs have no category and need asking. Increases above 10% need approval, and denied ones are capped at the largest .99 price within 10%. Original benchmark task written for usecaserank.com (not based on a specific public post). Headless, offline run on a reproducible fixture. Each run's notes say how its cost was measured (estimates are marked). Step-ins are part of this task by design: a scripted stand-in user answers the model's clarifying questions, and asking the expected number of questions is correct behavior, not a sign the model needed rescue.

Success criteria: 1. asked_missing_category_before_output 2. regular_prices_correct 3. missing_category_per_answer 4. increase_gate_before_output 5. denied_increases_capped 6. approved_increases_applied 7. no_needless_asks

Setup notes: Batch 14 case p10-price-change-rollout (offline, synthetic inputs, seed 168379806). Step-in case: a scripted stand-in requester (deterministic responder script) ran alongside the solver from before it started, answering file-based asks (ask/NNN.json -> answer/NNN.json); hidden key and responder kept outside the solver's work dir. Prompt = TASK.md verbatim. Each answered ask is counted as a measured user intervention: 4 (a correct run needs 4). No manual relays or chat redirects (headless run). Scored with the case's scorer (python3 score.py WORKDIR KEYDIR, absolute paths). Tools: Read/Write/Edit/Glob/Grep/Bash, web disabled. Single run, no retries. 1-min load average at scoring 0.35 (8 cores). Claude Code's own reported cost for claude-opus-5.5: $0.185919. Score: 7/7. Cost is an ESTIMATE: measured tokens x pricing.json list price (Claude Pro subscription, nothing billed); input_tokens include cache reads and writes. Single run.

Estimated cost List-price estimate; not actually billed.

Metrics

Estimated cost: $0.19
Wall time: 53s
Human step-ins: 4
Turns: 8

Model usage
Model	Input tok	Output tok	Cost
AClaude Opus 5.5anthropic/claude-opus-5.5effort default (no --effort flag) · thinking on	168,992	3,148	$0.19

Reproduce this run

Use the use case prompt on the use case page.
Same setup: Claude Opus 5.5 (anthropic/claude-opus-5.5, effort default (no --effort flag) · thinking on) via Claude Code 2.1.299 (headless -p), cloud.
It counts as a success only if it meets every success criterion: 1. asked_missing_category_before_output 2. regular_prices_correct 3. missing_category_per_answer 4. increase_gate_before_output 5. denied_increases_capped 6. approved_increases_applied 7. no_needless_asks
Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.

How runs are scored: Methodology.

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Automated checks

Trust 80% comes from these checks (how Trust % works).

Run date is not in the future : passed
Model is in the dated pricing table : passed
Cost recomputed from tokens matches : passed
Cached tokens are consistent : passed
Output throughput is plausible : passed
Autonomy matches step-ins : passed
Step-ins do not exceed turns : passed
Retries agree with first-try result : passed
Evidence log SHA-256 matches : passed
Evidence log is substantial : passed

Evidence log

[
 {
  "duration_api_ms": 46342,
  "stop_reason": "end_turn",
  "session_id": "[REDACTED]",
  "total_cost_usd": 0.18591859999999996,
  "usage": {
   "input_tokens": 16,
   "cache_creation_input_tokens": 11423,
   "cache_read_input_tokens": 157553,
   "output_tokens": 3148,
   "output_tokens_details": {
    "thinking_tokens": 368
   },
   "server_tool_use": {
    "web_search_requests": 0,
    "web_fetch_requests": 0
   },
   "service_tier": "standard",
   "cache_creation": {
    "ephemeral_1h_input_tokens": 11423,
    "ephemeral_5m_input_tokens": 0
   },
   "inference_geo": "global",
   "iterations": [
    {
     "input_tokens": 2,
     "output_tokens": 421,
     "cache_read_input_tokens": 23130,
     "cache_creation_input_tokens": 439,
     "cache_creation": {
      "ephemeral_5m_input_tokens": 0,
      "ephemeral_1h_input_tokens": 439
     },
     "type": "message"
    }
   ],
   "speed": "standard",
   "fallback_credit": null
  },
  "modelUsage": {
   "claude-opus-5-5": {
    "inputTokens": 16,
    "outputTokens": 3148,
    "cacheReadInputTokens": 157553,
    "cacheCreationInputTokens": 11423,
    "webSearchRequests": 0,
    "costUSD": 0.18591859999999996,
    "contextWindow": 1000000,
    "maxOutputTokens": 128000,
    "thinkingTokens": 368,
    "canonicalModel": "claude-opus-5-5",
    "provider": "firstParty",
    "costBasis": "list"
   }
  },
  "permission_denials": [],
  "terminal_reason": "completed",
  "fast_mode_state": "off",
  "fast_mode_disabled_reason": "sdk_opt_in_required",
  "subagent_stats": {
   "spawned": 0,
   "requested": {
    "background": 0,
    "foreground": 0,
    "unset": 0
   },
   "started_in_background": 0,
   "max_depth": 0,
   "spawned_by_subagents": 0,
   "completed": 0,
   "failed": 0,
   "killed": {
    "parent": 0,
    "user": 0,
    "system": 0
   },
   "refused": {
    "depth_limit": 0,
    "concurrency_limit": 0,
    "budget": 0
   },
   "by_type": {}
  },
  "safety_stops": 0,
  "is_error": false,
  "num_turns": 8,
  "subtype": "success",
  "api_error_status": null,
  "result": "I've written `new_prices.csv` for all 270 SKUs: 216 changed, 24 unchanged and 30 capped. The task said about 300, but the file has 270. I checked three rows by hand against the rules and they came out right, including one approved increase, one competitor-priced SKU and two capped ones.\n\n**What I asked the requester (4 asks, none repeated):**\n- **Missing categories (asks 001\u2013003):** three SKUs had no category. The answers were SKU20542 = garden, SKU20645 = electronics and SKU20675 = toys, and I priced them that way.\n- **Big-increase approval (ask 004):** I sent it after the category answers, since those three SKUs could become big increases too. It listed exactly the 60 SKUs whose new price was more than 10% above the current one, and went out before `new_prices.csv` existed. 30 were approved and use the full new price. 30 were denied and are capped at the highest .99 price no more than 10% above the current one. Two of the denied SKUs are SKU20645 and SKU20675, two of the three that had no category.\n\nAll the maths uses exact cents, so there's no floating-point rounding. `DONE.md` records the answers and the counts. The pricing script is at `/tmp/scripts/price.py`, outside the work folder.",
  "ttft_ms": 4516,
  "type": "result",
  "duration_ms": 52926,
  "uuid": "[REDACTED]",
  "ttft_stream_ms": 3200,
  "time_to_request_ms": 283,
  "first_content_frame_ms": 3201,
  "queued_turn_count": 0,
  "result_index": 0
 }
]